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Artificial Intelligence / Acinetobacter Infections · Journal article
Emerging Microbes & Infections · August 25, 2026
Encouraging direction, but not yet definitive.
This diagnostic study developed six machine learning models to predict carbapenem resistance in A. baumannii using MALDI-TOF MS spectral data. The XGBoost model achieved 96.36% accuracy on 191 clinical and surveillance isolates, with feature importance analysis suggesting resistance-associated proteomic signatures. However, the study lacks independent external validation and does not report clinical utility or comparison against standard testing turnaround.
Cross-sectional diagnostic study. Clinical and surveillance Acinetobacter baumannii isolates (carbapenem-resistant and carbapenem-susceptible) recovered from hospitalized patients.. Intervention: MALDI-TOF MS spectral acquisition combined with AI model prediction (XGBoost). Compared with: Phenotypic carbapenem susceptibility determined by standard minimum inhibitory concentration testing. n = 191. South Korea (Kyung Hee University); specific clinical sites not detailed..
XGBoost model achieved 96.36% accuracy in distinguishing CRAB from CSAB isolates Six AI models were systematically developed and compared for predictive performance Feature importance analysis identified horizontal gene transfer-related elements and membrane/transport-associated proteins as molecular correlates of carbapenem resistance
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Single-centre study of 191 isolates using AI-assisted MALDI-TOF MS for carbapenem resistance prediction in A. baumannii achieved 96.36% accuracy, but lacks validation in an independent cohort and clinical outcome data.
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Carbapenem-resistant Acinetobacter baumannii (CRAB) is one of the most critical public health threats worldwide due to its high infection rates, substantial mortality, and limited therapeutic choices. As CRAB infections are frequently multidrug-resistant, rapid and accurate determination of carbapenem susceptibility is essential for appropriate therapeutic decision-making. We established an integrated framework combining matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) with artificial intelligence (AI) to enable rapid prediction of carbapenem resistance in A. baumannii. A total of 191 clinical and surveillance isolates, including CRAB and carbapenem-susceptible A. baumannii (CSAB), were recovered from hospitalized patients and phenotypically characterized by standard minimum inhibitory concentration testing. MALDI-TOF MS spectra were subsequently acquired, and six AI models were developed and systematically investigated for predictive performance, among which the eXtreme Gradient Boosting (XGBoost) model achieved the highest discriminatory performance, distinguishing CRAB from CSAB with an accuracy of 96.36% and robust overall performance. Feature importance analysis of the XGBoost model revealed that its high predictive performance was driven partially by spectral features associated with horizontal gene transfer-related elements and membrane and transport-associated proteins, providing candidate molecular correlates of carbapenem resistance. Overall, these results show that AI-assisted MALDI-TOF MS enables rapid and accurate prediction of carbapenem resistance in A. baumannii and provides insights into the molecular features associated with resistance acquisition.
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